Ontology-based deep learning for human behavior prediction in health social networks

Nhathai Phan, Dejing Dou, Hao Wang, David Kil, Brigitte Piniewski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Human behavior prediction is a key component to studying the spread of wellness and healthy behavior in a social network. In this paper, we introduce an ontology-based Restricted Boltzmann Machine (ORBM) model for human behavior prediction in health social networks. We first propose a bottom-up algorithm to learn the user representation from ontologies. Then the user representation is used to incorporate self-motivation, social inuences, and environmental events together in a human behavior prediction model, which extends a well-known deep learning method, Restricted Boltzmann Machines (RBMs), so that the interactions among the behavior determinants are naturally simulated through parameters. To our best knowledge, the ORBM model is the first ontology-based deep learning approach in health informatics for human behavior prediction. Experiments conducted on both real and synthetic data from health social networks have shown the tremendous effectiveness of our approach compared with conventional methods.

Original languageEnglish (US)
Title of host publicationBCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages433-442
Number of pages10
ISBN (Electronic)9781450338530
DOIs
StatePublished - Sep 9 2015
Externally publishedYes
Event6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015 - Atlanta, United States
Duration: Sep 9 2015Sep 12 2015

Publication series

NameBCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Other

Other6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015
CountryUnited States
CityAtlanta
Period9/9/159/12/15

All Science Journal Classification (ASJC) codes

  • Software
  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

Keywords

  • Deep learning
  • Health informatics
  • Ontology
  • Social network

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